Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Efficient Selection of Activity Trajectories based on Diversity and Coverage
Authors: Chengcheng Yang, Lisi Chen, Hao Wang, Shuo Shang689-696
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two real-world datasets show that our proposal significantly outperforms state-of-the-art baselines. ... We conduct experiments on two real-world datasets. The experimental results demonstrate the efficiency and effectiveness of our proposal. |
| Researcher Affiliation | Academia | 1East China Normal University 2University of Electronic Science and Technology of China 3Nanjing University of Information Science and Technology |
| Pseudocode | Yes | Algorithm 1 presents the implementation of our method. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We experimented on two real-world datasets: TDrive [Yuan et al. 2011] and NYCTL [Donovan and Work 2015]. |
| Dataset Splits | Yes | In addition, 30%/10%/60% of the ground truth data was used for training/parameter tuning/testing. |
| Hardware Specification | Yes | We conducted experiments on a workstation powered by Intel Xeon Gold-6148 CPU on Linux (Ubuntu 16.04), having a Nvidia Titan Xp GPU. |
| Software Dependencies | No | The paper mentions "Linux (Ubuntu 16.04)" as the operating system, but does not provide specific version numbers for any other software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | The sampling size η was set as 10. For the Da ATS problem, we used the whole dataset and set the similarity threshold θ as 0.8. We randomly sampled 100 square-shape regions as the explored regions. By default, we set the region size as 0.01 of the city size and selected a subset of size 100. ... We tuned d using grid search and set d = 512. ... For self-attention, we searched hyperparameters in a wide range and found that d = 600 and u = 5 worked best. ... We tuned K with grid search and set K = 9 as it worked best. ... we divided the space into 200m 200m grid cells and set the maximum number of groups γ in each grid cell as 6. |